CHAPTER 1 7 . REGULATORY IMPACT ANALYSIS

TABLE OF CONTENTS

17.1 INTRODUCTION

................................ ................................ ................................ ... 17

- 1

17.2 NON

- REGULATORY POLICIES

................................ ................................ .......... 17

- 2

17.2.1 METHODOLOGY

................................ ................................ ................................ .. 17

- 2

17.2.2 ASSUMPTIONS REGARDIN G NON

- REGULATORY POLICIES

..................... 17

- 3

17.2.3 POLICY INTERACTIONS

................................ ................................ ..................... 17

- 4

17.3 NON

- REGULATORY POLICY ASSUMPTIONS

................................ ................ 17

- 4

17.3.1 NO NEW REGULATORY AC TION

................................ ................................ ..... 17

- 4

17.3.2 CONSUMER REBATES

................................ ................................ ......................... 17

- 4

17.3.2.1 Methodology

................................ ................................ .......................... 17

- 5

17.3.2.2 Analysis

................................ ................................ ................................ .. 17

- 6

17.3.3 CONSUMER TAX CREDITS

................................ ................................ ................ 17

- 9

17.3.4 MANUFACTURER TAX CRE DITS

................................ ................................ .... 17

- 13

17.3.5 VOLUNTARY ENERGY EFF ICIENCY TARGETS

................................ .......... 17

- 14

17.3.6 EARLY REPLACEMENT

................................ ................................ .................... 17

- 16

17.3.7 BULK GOVERNMENT PURC HA SES

................................ ................................ 17

- 20

17.4 IMPACTS OF NON

- REGULATORY ALTERNATI VES

................................ ... 17

- 21

LIST OF TABLES

Table 17.1.1 Non

- Regulatory Alternatives to National Standards

................................ ...... 17

- 1

Table 17.2.1 Efficiency Levels for Trial Standard Levels for Commercial Clothes Washers Equipment Classes

................................ ................................ ........... 17

- 3

Table 17.3.1 Benefit/Cost Ratios Without and With Rebates for Commercial Clothes

Washer

................................ ................................ ................................ ............ 17

- 7

Table 17.3.2 Market Penetrations in 2018 Attributable to Consumer Rebates for

Commercial Clothes Washers

................................ ................................ ......... 17

- 9

Table 17.3.3 Oregon Tax Credits Claimed for Residential Clothes Washers from 2000

to 2011

................................ ................................ ................................ .......... 17

- 10

Table 17.3.4 Percentages of Increase in Total Shipments of Commercial Clothes Washers Attributed to Tax Credits, Rebates and other Market Ef fects

........ 17

- 12

Table 17.3.5 Market Penetrations in 2018 Attributable to Consumer Tax Credits for

Commercial Clothes Washers

................................ ................................ ....... 17

- 13

Tab le 17.3.6 Market Penetrations in 2018 Attributable to Manufacturer Tax Credits

for Commercial Clothes Washers

................................ ................................ . 17

- 14

Table 17.3.7 Market Penetrations in 2018 Attributable to Voluntary Energy

Eff iciency Targets for Commercial Clothes Washers

................................ .. 17

- 15

17

- iTable 17.3.8 Market Penetrations in 2018 Attributable to Early Replacements for

Commercial Clothes Washers

................................ ................................ ....... 17

- 19

Table 17.3.9 Market Penetrations in 2018 Attributable to Bulk Government Purchasing of Commercial Clothes Washers

................................ ................ 17

- 21

Table 17.4.1 Impacts of Non

- Regulator y Alternatives for Front

- Loading Commercial Clothes Washers (TSL 2)

................................ ................................ .............. 17

- 23

Table 17.4.2 Impacts of Non

- Regulatory Alternatives for Top

- Loading Commercial Clothes Washers (TSL 2)

................................ ................................ .............. 17

- 24

LIST OF FIGURES

Figure 17.3.1 Market Penetration Curves for Commercial Clothes Washers

....................... 17

- 8 Figure 17.3.2 Base Case and Policy Case Shipments Projections for the Early Replacement Policy Case (thousand units)

................................ ................... 17

- 19 Figure 17.4.1 Market Penetration of Front

- Loading Commerci al Clothes Washers Meeting the Target Level in Policy Cases (TSL 2)

................................ ...... 17

- 22 Figure 17.4.2 Market Penetration of Top

- Loading Commercial Clothes Washers Meeting the Target Level in Policy Cases (TS L 2)

................................ ...... 17

- 22

17

- iiCHAPTER 17. REGULATORY IMPACT AN ALYSIS

17.1 INTRODUCTION

The U.S. Department of Energy (DOE) has determined that the regulatory action described in the Federal Register notice associated with this TSD constitute s an “economicall y significant regulatory action” under Executive Order (E.O.) 12866

, Regulatory Planning and Review. 58 FR 51735 (October 4, 1993). For such actions, E.O. 12866 requires Federal agencies to provide “a n assessment, including the underlying analysis, of cos ts and benefits of potentially effective and reasonably feasible alternatives to the planned regulation, identified by the agencies or the public (including improving the current regulation and reasonably viable non

- regulatory actions), and an explanation why the planned regulatory action is preferable to the identified potential alternatives. ” 58 FR 51735, 51741.

To conduct this analysis , DOE used an integrated National Impact Analysis ( NIA )

- RIA

model built on the NIA model discussed in C hapter 11 for it s analysis.

DOE identified six non

- regulatory policy alternatives that possibly could provide

incentives for the same energy efficiency levels as the ones in the trial standard level s (TSL) for

the commercial clothes washers that are the subject of thi s rulemaking. The non

- regulatory policy alternatives are listed in Table 17.1.1 , which also includes the “no new regulatory action”

alternative. DOE evaluated each alternative in terms of its ability to achieve significant e nergy savings at a reasonable cost, and compared the effectiveness of each to the effectiveness of the

selected standard s

.

Table 17.1.1 Non

- Regulatory Alternatives to National Standards No New Regulatory Action

Consumer Rebates

Consumer Tax Credits

M anufacturer Tax Credits Voluntary Energy Efficiency Targets Early Replacement

Bulk Government Purchases

Sections 17.2 and 17.3 discuss the analysis of the six selected policies listed above ( excluding the alternative of no new regulatory action)

. Section 17.4 present s the results of the policy alternatives

.

17

- 117.2 NON

- REGULATORY POLICIES

This section describes the method DOE used to analyze the energy savings and cost

ef

fectiveness of the non

- regulatory policy alternatives for commercial clothes washers. This

section also describes the assumptions underlying the analysis.

17.2.1 METHODOLOGY

DOE used its integrated NIA

- RIA spreadsheet model to calculate the national energy

sav ings (NES) and net present value (NPV) associated with each non

- regulatory policy

alternative . Chapter 1 1 of the technical support document (TSD) describes the NIA spreadsheet

model . Appendix 17

- A

, discusse s the NIA

- RIA integrated model approach.

DOE qua ntified the effect of each alternative on the purchase of equipment that meet the efficiency level s corresponding to the levels set for each TSL . After establishing the quantitative assumptions underlying each alternative, DOE appropriately revised inputs to the NIA

- RIA spreadsheet model . The primary model inputs revised were market shares of equipment meeting

the target efficiency level s set for each TSL . The shipments of equipment for any given year reflect a distribution of efficiency levels. DOE assumed , for each TSL, that new energy efficiency standard s would affect 100 percent of the shipments of equipment that did not meet

the TSL target level in the base case, a whereas the non

- regulatory policies would affect a smaller percentage of those shipments. DOE made certain assumptions about the percentage of shipments affected by each alternative policy

. DOE used those percentages to calculate the shipment

- weighted average energy consumption and costs of commercial clothes washers attributable to each policy alternative.

Increasing the efficiency of equipment often increases its average installed cost.

However , operating costs generally decrease because energy consumption declines. DOE

therefore calculated an NPV for each non

- regulatory alternative in the same way it did for the

selected standard s. In some policy scenarios, increases in total installed cost are mitigated by government rebates or tax credits. Because DOE assumed that consumers would re

- pay credits and rebates in some way (such as additional taxes) , DOE did not include rebates or tax credits as a consumer benefit when calculating national NPV. DOE’s analysis also excluded any

administrative costs for the non

- regulatory policies; including such costs would decrease the NPVs slightly.

The fo llowing are key measures for evaluating the impact of each alternative.

• National Energy Savings (NES) , given in quadrillion Btus (quads), describes the cumulative national primary energy sav ed over the lifetime of equipment purchased

a The base case for the NIA is a market

- weighted average energy efficiency calculated from units at several efficiency levels.

17

- 2during the 30

- year a nalysis period starting in the effective date of the policy ( 201 8

- 2047 ).

• Net Present Value (NPV)

, represents the value of net monetary savings in 2014

, expressed in 2013$

, from equipment purchased during the 30

- year analysis period starting in the effect ive date of the policy ( 201 8

- 2047 )

. DOE calculated the NPV as

the difference between the present value of installed equipment cost and operating expenditures in the base case and the present value of th o se costs in each policy case. DOE calculated operatin g expenses (including energy costs ) for the life of equipment

.

17.2.2 ASSUMPTIONS REGARDIN G NON

- REGULATORY POLICIES

The effects of non

- regulatory policies are by nature uncertain because they depend on program implementation

, marketing efforts

, and on consumers ’ responses to a program. Because the projected effects depend on assumptions regarding the rate of consumer participation, they are subject to more uncertainty than are the impacts of mandatory standards, which DOE

assumes will be met with full compliance . To increase the robustness of the analysis, DOE conducted a literature review regarding each non

- regulatory policy and consulted with recognized experts to gather information on similar incentive programs that have been implemented in the United States. By studying experiences with the various types of programs, DOE sought to make credible assumptions regarding potential market impacts . Section 17.3 presents the sources DOE relied on in developing assumptions a bout each alternative policy and reports DOE’s conclusions as they affected the assumptions that underlie the modeling of each

alternative policy.

Each non

- regulatory policy that DOE considered would improve the average efficiency

of new commercial cloth es washers relative to their base case efficiency scenario (which

involve s no new regulatory action). The analysis considered that each alternative policy would induce consumers to purchase units h aving the same efficiency level as required by the selected

standard s (the target level ) set for each TSL . As opposed to the standards case, however, the policy cases may not lead to 100 percent market penetration of units that meet the target level

.

Table 17.2.1 show s the efficiency level stipulated in the selected standard s for

commercial clothes washers

.

Table 17.2.1 Efficiency Levels for Trial Standard Levels for Commercial Clothes Washers Equipment Classes

Equipment Type Efficiency Levels ( MEF J2 /IWF)

Baseline

FL * : EL0

TL * : EL0 TSL1 TSL2 TS L3 TSL4

FL: EL1

TL: EL1 FL: EL2

TL: EL1 FL: EL2

TL: EL2 FL: EL3

TL: EL2
Front Loading CCW

Units 1.65/5.2 1.80/4.5 2.00/4.1 2.00/4.1 2.20/3.9

17

- 3Top Loading CCW

Units 1.15/8.9 1.35/8.8 1.35/8.8 1.55/6.9 1.55/6.9

* FL: Front Loading. TL: Top Loading.

DO E assumed that the effects of non

- regulatory policies would last from the effective

date of standards

— 201 8

— through the end of the analysis period, which is 2047

.

17.2.3 POLICY INTERACTIONS

DOE calculated the effects of each non

- regulatory policy separately fro m those of the other policies. In practice, some policies are most effective when implemented in combination, such as early replacement implemented with consumer rebates, or early replacement implemented with bulk government purchases. However, DOE attempt ed to make conservative assumptions to avoid double

- counting policy impacts. The resulting policy impacts are not

additive ; the combined effect of several or all policies cannot be inferred from summing their

results.

Section 17.4 presents graphs that show the market penetration estimated under each non

regulatory policy for the commercial clothes washer equipment class es

.

17.3 NON

- REGULATORY POLIC Y ASSUMPTIONS

The following subsections describe DOE’s analysis of the impacts of the six non

regulatory policy alternatives to the selected standard s for commercial clothes washers

. (Because the alternative of No New Regulatory Action has no energy or economic impacts, essentially representing the NIA base case, DOE did not perform any additional analysis for that alternative.) DOE developed estimate s of the market penetration of high

- efficiency equipment

both with and without each of the non

- regulatory policy alternatives

.

17.3.1 NO NEW REGULATORY AC TION

The case in which no new regulatory a ction is taken with regard to the energy efficiency

of commercial clothes washers constitutes the base case, as described in C hapter 1 1

, National Impact Analysis. The base case provides the basis of comparison for all other policies. By definition, no new regulatory action yields zero NES and an NPV of zero dollars.

17.3.2 CONSUMER REBATES

DOE considered th e scenario in which the Federal government would provide financial

incentives in the form of rebate s to consumers for purchasing energy

- efficient appliances

. Th is policy provides a consumer rebate for purchasing commercial clothes washers that operate at (or above) the same efficiency as stipulated in each TSL ( target level )

.

17

- 417.3.2.1 Methodology

DOE based its evaluation methodology for consumer rebates on a comprehensive study of California’s potential for achieving energy efficiency

. Th e study, performed by XENERGY,

Inc., b summarize d experiences with various utility rebate programs. 1 XENE RGY’s analytical

method utilized graphs, or penetration curves
, that estimate the market penetration of a technology based on its benefit/cost (B/C) ratio. DOE consulted with experts and reviewed other methods of estimating the effect of consumer rebate programs on the market penetration of efficient technologies. The other methods, developed after the referenced XENERGY report was published, 2

, 3

, 4

, 5

, 6

, 7 used different approaches: other economic parameters (e.g., payback period), expert surveys, or model calib ration based on specific utility program data rather than

multi

- utility data

. Some models in use by energy efficiency program evaluation experts were so

client

- specific that generic relationships between economic parameters and consumer res ponse
could not be established. 5 DOE decided that the most appropriate available method for this RIA analysis was the XENERGY approach of penetration curves based on B/C ratio, which incorporates lifetime operating cost savings

.

XENERGY’s model estimates market impact s induced by financial incentives based on the premise that two types of information diffusion drive the adoption of new technologies.

Internal sources of information encourage consumers to purchase new equipment primarily through word

- of

- mouth from early adopters. External sources affect consumer purchase decisions through marketing efforts and information from outside the consumer group. Appendix

17

- A contains additional details on internal and external information diffusion.

XENERGY’s model equation acc ounts for the influences of both internal and external sources of information by superimposing the two components. Combining the two mechanisms for information diffusion, XENERGY’s model generates a set of penetration (or implementation)

curves for a polic y measure. XENERGY calibrated the curves based on participation data from utility rebate programs. The curves illustrate the increased penetration (i.e., increased market share) of efficient equipment driven by consumer response to changes in B/C ratio ind uced by rebate programs. The penetration curves depict various diffusion patterns based on perceived

market barriers (from no

- barriers to extremely

- high

- barriers ) to consumer purchase of high

efficiency equipment

. DOE adjusted the XENERGY former penetratio n curves based on expert advice founded on more recent utility program experience. 5

, 8

DOE modeled the effects of a consumer rebate policy f or commercial clothes washers by determining , for each TSL, the increase in market penetration of equipment meeting the target

level relative to their market penetration in the base case. It used the interpolation method presented in Blum et al (2011) 9 to create customized penetration curves based on relationships

between actual base case market penetrations and actual B/C ratios. To inform its estimate of B/C ratios provided by a rebate program DOE performed a thorough nationwide search for existing rebate programs for commercial clothes washers . It gathered data on utility or ag ency

b XENERGY is now owned by KEMA, Inc. ( www.ke ma.com )

17

- 5rebates throughout the nation for this equipment , and used this data to calibrate the customized penetration curves it developed for front

- loading and top

- loading commercial clothes washers so they can best reflect the market barrier level faced by ea ch equipment class

. Section 17.3.2.2 shows the interpolated curve s used in the analysis.

17.3.2.2 Analysis

For the two commercial clothes washer equipment class es it analyzed , DOE estimated t he effect of increasing its B/ C ratio via a rebate that would pay all o f the incr eased installed cost of

a unit that met the target efficiency level compared to one meeting the baseline efficiency l evel

. c

To inform its estimate of an appropriate rebate amount, DOE performed a thorough nationwide search for existing rebate pro grams for commercial clothes washers

. It gathered data from a sample of utility and agency rebate programs that includes 38 rebates for commercial clothes

washers initiated by 26 utilities or agencies in various States d

. (Appendix 17

- A

, identifies the

reba te programs .) To represent the rebate level, DOE used t he simple average of the rebate

amounts in these programs. DOE assumed th at rebates would remain in effect at the same levels throughout the forecast period ( 201 8

– 2047 ).

For each of the two commerc ial clothes washer equipment classes

, DOE first calculated

the B/C ratio without a rebate using the difference in total installed costs (C) and lifetime operating cost savings (B) between the unit meeting the target level and the baseline unit. It then

cal culated the B/C ratio given a rebate for the unit meeting the target efficiency level

. Because the rebate reduced the incremental cost, the unit receiving the rebate had a l arger B/C ratio. Table

17.3.1 show s the effect of con s umer rebates on the B/C ratio for each TSL

.

c The baseline technology for each product class is defined in the engineering analysis, Chapter 5, as the technology that represents the basic characteristics of products in that class

. A baseline unit typically is one that just meets

current Fed eral energy conservation standards and provides basic consumer utility

.

d Some rebates for commercial clothes washers are offered by electric and /or gas utilities, some by water utilities

and

, in some service territories

, by both energy and water utilitie s

. DOE calculated t he avera ge rebate amount from the total (combined) rebate amount available to the consumer.

17

- 6Table 17.3.1 Benefit/Cost Ratios Without and W ith Rebates for Commercial Clothes

Washer

Trial Standard Levels

Front

- Loading 1 2 3 4

B/C Ratio Without Rebate infinite 340.4 340.4 7.2 Rebate Amount (2013$) 160.47 160.47 1 60.47 160.47

B/C Ratio With Rebate infinite infinite infinite infinite

Estimated Market Barriers xHigh * Mod

- High * Mod

- High xHigh Top

- Loading 1 2 3 4

B/C Ratio Without Rebate infinite infinite 12.3 12.3 Rebate Amount (2013$) 160.47 160.47 160.47 160.47

B/C Ratio With Rebate infinite infinite infinite infinite

Estimated Market Barriers xHigh xHigh H i gh

- xHigh * High

- xHigh

* xHigh : E xtremely

- H igh

. Mod

- High: Moderate

- to

- High. High

- xHigh: High

- to

- Extremely

- High

DOE used the se B/C ratios along with the penetration curve s shown in Figure 17.3.1 to estimate the percentage of consumers who would purchase commercial clothes washers that meet

the target level both with and without a rebate incentive. e The penetration curve s calcul ated by DOE to represent the market behavior for front

- loading and top

- loading commercial clothes

washers at each TSL are indicated in Table 17.3.1

.

Front

- Loading Top

- LoadingTSL 1

e Most of the graphs presented in Figure 17.3.1 do not show the ‘No Rebate’ and ‘With Rebate’ data points as the

B/C ratio for those data points are either infinite or substantially greater than 200.

17

- 7TSL 2TSL 3TSL 4

Figure 17.3.1 Marke t Penetration Curves for Commercial Clothes Washers

For each equipment class, DOE next estimated the percent increase s represented by the change in penetration rate shown on the corresponding penetration curve. It then added this percent increase to t he market share of units that meet the target level in the base case to obtain the market share of units that meet the target level in the rebate policy case.

Table 17 .3.2 summarizes DOE’s assumptions for commer cial clothes washers regarding the market penetration of products in 2018 that meet the target efficiency level at each TSL given

a consumer rebate.

17

- 8Table 17.3.2 Market Penetrations in 201 8 Attributable to Consumer R ebates for

Commercial Clothes Washers

Trial Standa rd Levels

Front

- Loading 1 2 3 4

Base

- Case Market Share 31.0% 69.0% 69.0% 0.0% Policy Case Market Share 31.0% 69.9% 69.9% 50.0%

Increased Market Share 0.0% 0.9% 0.9% 50.0%
Top

- Loading 1 2 3 4

Base

- Case Market Share 26.7% 26.7% 11.3% 11.3% Policy Case Market Share 50.0% 50.0% 51.4% 51.4%

Increased Market Share 23.3% 23.3% 40.1% 40.1%

DOE used the resulting annual increases in market shares as inputs to represent the rebate policy case scenario in its NIA

- RIA model. Appendix 17

- A shows the annual m arket share increases due to this policy. Section 17.4 presents the resulting market penetration trends for the policy case of consumer rebates for commercial clothes washers.

17.3.3 CONSUMER TAX CREDITS

In preparing i ts assumptions to estimate the effects of tax credits on consumer purchases of commercial clothes washers, DOE reviewed tax credit programs that have been offered at the Federal level for energy

- efficient appliances. The Energy Policy Act of 2005 (EPACT 20 05 )

included Federal tax credits for consumers who purchase energy

- efficient equipment. 10 Those

tax credits were in effect in 2006 and 2007, expired in 2008, were reinstated for 2009

– 2010 by

the American Recovery and Reinvestment Act of 2009 ( ARRA ), extended by Congress for 2011

with some modifications , and expired at the end of 2011

. 11

, 12 The Americ an Taxpayer Relief
Act of 2012 extended, with some modifications, residential tax credits for air conditioners, heat pumps, furnaces, and water heaters placed in service between January 1, 2012 and December 31,

2013. 13 DOE reviewed Internal Revenue Service data on the numbers of taxpayers who claimed the tax credits during tax years 2006 and 2007. DOE also reviewed data from an earlier Federal energy conservation tax credit program in place in the 1980s. However, D OE did not find data specific enough to commercial clothes washers to warrant its analysis for the c onsumer t ax

c redits policy case. Appendix 17

- A contains more information on Federal consumer tax credits.

DOE estimated the effects of tax credits on con sumer purchases using data from Oregon State’s tax credit program s for energy

- efficient appliances

. ( Additional discussion of State tax credits for Oregon and other states is in Appendix 17

- A .) Oregon has had consumer tax credits for efficient appliances f or several years. f DOE reviewed Oregon’s business energy tax credit (BETC) for commercial clothes washers. Oregon’s BETC would typically cover 35

– 50 percent

f The District of Columbia (Washington D.C.) also passe d a piece of legislation entitled the “ Residential Energy

Conservation Tax Credit Act Of 2005 ” for consumer tax cre dits, but did not appropriat e the necessary funding

and so the credits did not go into effect.

17

- 9of the incremental cost of the efficient equipment. g The majority of these credits w as claimed for laundries and rental dwellings, and included commercial clothes washers as well as other appliances. However, ODOE did not track the types of appliances involved nor the number of machines per project; the annual counts are the total number of projects, wi th each project involving one or more appliances. DOE was therefore unable to use the data to estimate market percentages for specific appliances, i.e. commercial clothes washers. However, DOE did note the trend of increasing numbers of credits claimed fro m 1998 through 2006 for residential clothes

washers , which contributed to its assumptions regarding the trend line for this policy scenario.

Table 17.3.3 Oregon Tax Credits Claimed for Residential Clothes Washers from 2000 to

201 1

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Tax Credits* 8

. 2 12 .2 16

. 2 19

. 7 18

. 5 23 .4 25

. 5 23

. 6 23

. 8 23 .3 29

. 7 24

. 6
Shipments** 86

. 4 88

. 3 91

. 0 97

. 7 102

. 0 101 .8 106 .5 118 .2 137 .6 145 .9 155

. 1 142

. 1
Percentage*** 9.5 13.8 17.8 20.2 18.2 23.0 24.0 20.0 17.3 16.0 19.1 17.3

* Number of t ax c redit s claimed (thousand units)

. ** Shipments of clothes washers to Oregon (thousand units)

. *** Percentage of shipments to Oregon with tax credits.

Because tax credits have not been available for commercial clothes washers, DOE analyzed the Oregon residential tax credit data for residential clothes washers to inform the analysis of the tax credit policy for commercial clothes washers. DOE obtained data from the Oregon Department of Energy (ODOE) on the number of efficient appliances for w hich tax credits were claimed by Oregon residential taxpayers for the years 1998 through 2012, including

those f or residential clothes washers

. 14 S ince the tax credit for clothes washers expired at the end

of 2011 , DOE assumed that the 2012 credits in ODOE’s database were for systems sold prior to

2012. DOE also obtained shipments data for clothes washers by state (including Oregon) for the

years 2000 through 2012 from the Association of Home Appliance M anufacturers (AHAM). 15 It

then calculated the percentages of clothes wa shers sold in Oregon for which taxpayers claimed credits for the years 2000 through 2011

. Table 17.3.3 shows the number of Oregon residential tax credits claimed for clothes washers as percentages of total annual shipments these claims represent for the ye ars 2000

– 2011. DOE used those percentages to estimate the percentage of shipments of commercial clothes washers that would meet the efficiency level at each TSL due to a tax credit program for 2018 through 2029

.

N ot all of these high

- efficiency clothes wa shers , however, w ould likely be purchased due to the tax credit program alone. Some taxpayers would be motivated by utility rebate incentives, and others would be "free riders" who would purchase efficient appliances without a tax credit or

rebate incentiv e but still claimed the tax credit. To better understand this interaction between tax credits, rebates, and free riders/market impacts, in its previous analysis DOE reviewed a report

g The business tax credit is taken over five years: 10 percent in the first and second years and 5 percent each year

thereafter; the taxpayer may carry the unu sed credit forward up to eight years. Those with eligible project costs of $20,000 or less may take the tax credit in one year.

17

- 10prepared by KEMA on efficient clothes washers in the Northwest. KEMA comp ared the penetration of “ultra high efficiency” (UHE) h clothes washers in Oregon and Washington, two neighboring States that have similar utility rebate programs for residential clothes washers

. 16 The study attributed the difference in market share of these UHE un its (50 percent for Oregon versus 15 percent for Washington) to the Oregon tax credits.

DOE relied on this conclusion to estimate the increase in market share of high

- efficiency clothes washers due to a consumer tax credit program only. For each equipme nt class, DOE first

removed the potential contributions of rebates and free riders to that increase

. For rebates, DOE used the same market penetration it estimated above for the rebate policy case ; f or free riders

DOE used the base case market share . Since both the rebate policy case and the base case market shares are constant, the resulting percentages for front

- loading and top

- loading commercial clothes washers for the years 2018 through 2029 have different values yet same shape. They both present an inc reasing trend for the first seven years and a d e creasing trend for the remaining five

years. DOE did some interpolations to smooth the shapes of the two market penetration curves for the twelve initial years, and extrapolated the decreasing trend of market s hare for years beyond 2029 until that share reaches zero. Table 17.3.4 show s the percentages of increase in total shipments attributed to the tax credits themselves, to utility rebates, and to other market

influences/free rid ers for 2018 through 2029 for each TSL.

h Units meeting specifications of minimum modified energy factor ( MEF J2 ) of 2.20 and maximum water factor

(WF) of 6.5.

17

- 11Table 17.3.4 P ercentages of I ncrease in T otal S hipments of Commercial Clothes Washers

A ttributed to T ax C redits , Rebates and other Market Effects

20 18 201 9 202 0 20 21 20 22 20 23 20 24 20 25 20 26 20 27 20 28 20 29

TSL 1

Front

- L oadin g

Rebates 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Market effects 2.9% 4.3% 5.5% 6.3% 5.6% 7.1% 7.4% 6.2% 5.4% 4.9% 5.9% 5.4%

Tax Credits 6.6% 9.5% 12.3% 13.9% 12.5% 15.8% 16.6% 13.8% 11.9% 11.0

% 13.2

% 12.0

%
Top

- Loading

Rebates 2.2% 3.2% 4.1% 4.7% 4.2% 5.3% 5.6% 4.7% 4.0% 3.7% 4.5% 4.0%

Market effects 2.5% 3.7% 4.8% 5.4% 4.9% 6.1% 6.4% 5.4% 4.6% 4.3% 5.1% 4.6%

Tax Credits 4.7% 6.9% 8.9% 10.1% 9.1% 11.5% 12.0% 10.0% 8.7% 8.0% 9.6% 8.7%

TSL 2

Front

- Loading

Rebates 0.1% 0.1% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.1% 0.2% 0.2%

Market effects 6.6% 9.5% 12.3% 13.9% 12.5% 15.8% 16.6% 13.8% 11.9% 11.0

% 13.2

% 12.0

%

Tax Credits 2.9% 4.2% 5.4% 6.1% 5.5% 6.9% 7.2% 6.0% 5.2% 4.8% 5.8% 5.2%
Top

- Loading

Rebates 2.2% 3.2% 4.1% 4.7% 4.2% 5.3% 5.6% 4.7% 4.0% 3.7% 4.5% 4.0%

Market effects 2.5% 3.7% 4.8% 5.4% 4.9% 6.1% 6.4% 5.4% 4.6% 4.3% 5.1% 4.6%

Tax Credits 4.7% 6.9% 8.9% 10.1% 9.1% 11.5% 12.0% 10.0% 8.7% 8.0% 9.6% 8.7%

TSL 3

Front

- Loading

Rebates 0.1% 0.1% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.1% 0.2% 0.2%

Market effects 6.6% 9.5% 12.3% 13.9% 12.5% 15.8% 16.6% 13.8% 11.9% 11.0

% 13.2

% 12.0

%

Tax Credits 2.9% 4.2% 5.4% 6.1% 5.5% 6.9% 7.2% 6.0% 5.2% 4.8% 5.8% 5.2%
Top

- Loading

Rebates 3.8% 5.5% 7.2% 8.1% 7.3% 9.2% 9.6% 8.0% 6.9% 6.4% 7.7% 7.0%

Market effects 1.1% 1.6% 2.0% 2.3% 2.0% 2.6% 2.7% 2.3% 1.9% 1.8% 2.2% 2.0%

Tax Credits 4.6% 6.7% 8.7% 9.8% 8.8% 11.2% 11.7% 9.7% 8.4% 7.8% 9.3% 8.4%

TSL 4

Front

- Loading

Rebates 4.7% 6.9% 8.9% 10.1% 9.1% 11.5% 12.0% 10.0% 8.7% 8.0% 9.6% 8.7%

Market effects 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Tax Credits 4.7% 6.9% 8.9% 10.1% 9.1% 11.5% 12.0% 10.0% 8.7% 8.0% 9.6% 8.7%
Top

- Loading

R ebates 3.8% 5.5% 7.2% 8.1% 7.3% 9.2% 9.6% 8.0% 6.9% 6.4% 7.7% 7.0%

Market effects 1.1% 1.6% 2.0% 2.3% 2.0% 2.6% 2.7% 2.3% 1.9% 1.8% 2.2% 2.0%

Tax Credits 4.6% 6.7% 8.7% 9.8% 8.8% 11.2% 11.7% 9.7% 8.4% 7.8% 9.3% 8.4%

Table 17 .3.5 summarizes DOE’s assumptions for commercial clothes washers regarding

the market penetration of units in 2018 that meet the efficiency level at each TSL given a

consumer tax credit.

17

- 12Table 17.3.5 Market Penetrations in 201 8 Attributable to Consumer Tax Credits for

Commercial Clothes Washers

Trial Standard Levels

Front

- Loading 1 2 3 4

Base

- Case Market Share 31.0% 69.0% 69.0% 0.0% Policy Case Market Share 38.2% 72.2% 72.2% 5.2%

Increased Market Share 7.2% 3.2% 3.2% 5.2%
Top

- Loading 1 2 3

Base

- Case Market Shar e 26.7% 26.7% 11.3% 11.3% Policy Case Market Share 32.0% 32.0% 16.4% 16.4%

Increased Market Share 5.2% 5.2% 5.1% 5.1%

The increased market shares attributable to consumer tax credit s shown in Table 17.3.5 were used as inp uts in the NIA

- RIA model. Appendix 17

- A shows the annual market share increases due to this policy. Section 17.4 presents the resulting market penetratio n trends for the policy case of consumer tax credits for commercial clothe s washers that meet the efficiency level

for each TSL

.

17.3.4 MANUFACTURER TAX CRE DITS

To analyze the potential effects of a policy that offers tax credits to manufacturers that produce commercial clothes washers that meet the target efficiency level at each TSL

, DOE attempted to investigate manufacturer response to the Energy Efficient Appliance Credits for manufacturers mandated by EPACT 2005. 17 Those manufacturer tax credits have been in effect for dishwashers, clothes washers and refrig erators produced beginning in 2009. (Appendix 17

- A presents details on Federal manufacturer tax credits.) DOE was unable to locate data from the Internal Revenue Service or other sources on manufacturer response to the Federal credits.

DOE then estimated the effects of tax credits to manufacturers based on its previous analysis of consumer participation in tax credits. DOE incorporated previous research that disaggregated the effect of rebates and tax credits into a direct price effect , which derives from the savings in purchase price, and an announcement effect , which is independent of the amount

of the incentive. 18

, 19 The announcement effect derives from the credibility that a technology receives from being includ ed in an incentive program, as well as changes in product marketing and modifications in markup and pricing. DOE assumed that rebate and consumer tax credit policies would encompass both direct price effects and announcement effects, and that half of the

i ncrease in market penetration associated with either policy would be due to the direct price

effect and half to the announcement effect. DOE th e n assumed that a manufacturer tax credit

would lead manufacturers to pass on some of their reduced costs to cons umers, causing a direct price effect. However, no announcement effect would occur because the program would not be

17

- 13visible to consumers. i Because the direct price effect is approximately equiva lent to the

announcement effect

, 18 DOE estimated that a manufacturer tax credit would induce half the number of consumers assumed to take advantage of a consumer tax credit to purcha se more efficient equipment

. DOE thus applied 50 percent to the increase in market penetrations

estimated above for consumer tax credits (See Table 17.3.5 ) and used the resulting market penetrations to represent the impact s from manufacturer tax credits. Table 17.3.6 summarize s DOE’s assumptions for commercial clothes washers regarding the market penetration of units in

201 8 meeting the target efficiency level at each TSL given a manufacturer tax credit.

Table 17.3.6 Market Penetrations in 201 8 Attributable to Manufacturer Tax Credits for

Commercial Clothes Washers

Trial Standard Levels

Front

- Loading 1 2 3 4

Base

- Case Market S hare 31.0% 69.0% 69.0% 0.0% Policy Case Market Share 34.6% 70.6% 70.6% 2.6%

Increased Market Share 3.6% 1.6% 1.6% 2.6%
Top

- Loading 1 2 3 4

Base

- Case Market Share 26.7% 26.7% 11.3% 11.3% Policy Case Market Share 29.4% 29.4% 13.8% 13.8%

Increased Marke t Share 2.6% 2.6% 2.6% 2.6%

The increased market shares attributab le to a manufacturer tax credit shown in Table

17.3.6 were used as inputs in the NIA

- RIA model. Appendix 17

- A , shows the annual market share increases due to this policy

. Section 17.4 presents the resulting market penetratio n trends

for the policy case of manufacturer tax credits for commercial clothes washers

.

17.3.5 VOLUNTARY ENERGY EFF ICIENCY TARGETS

For each equipment class , DOE assumed that voluntary energy effi ciency targets would be achieved as manufacturers gradually stopped producing units that operated below the efficiency level set for each TSL . DOE assumed that the impetus for phasing out production of

low

- efficiency units would be a program similar to the ENERGY STAR labeling program conducted by the Environmental Protection Agency (EPA) and DOE. The ENERGY STAR

program specifie s the minimum energy efficienc ies that various products must have to receive

the ENERGY STAR label . ENERGY STAR encourages consume rs to purchase efficient products via marketing that promotes consumer label recognition, various incentive programs that adopt the ENERGY STAR specifications, and manufacturers’ promotion of their qualifying appliances. ENERGY STAR projects market penetra tion of compliant appliances and estimates

i Note t hat this is a conservative assumption, since it is possible that manufacturers or utility/agency efficiency programs might promote the models for which manufacturers increase production due to the tax credits, which in turn might induce some announcement e ffect. However, DOE found no data on such programs on which to base an estimate of the magnitude of this possible announcement effect on consumer behavior.

17

- 14the percentage of sales of compliant appliances that are attributable to the ENERGY STAR

program.

Researchers have analyzed the ENERGY STAR program’s effects on sales of several consumer products. Program effo rts generally involve a combination of information dissemination and utility or agency rebates. The analyses have been based on State

- specific data

on percentages of shipments of various appliances that meet ENERGY STAR specifications. The analyses general ly have concluded that the market penetration of ENERGY STAR

- qualifying appliances is higher in regions or States where ancillary promotional programs have been

active

. 20

, 21

, 22

DOE based its estimates of market penetration on its previous estimates of the impact of a voluntary energy efficiency targets p rogram on shipments of commercial clothes washers

. In the absence of data on the impacts of the existing ENERGY STAR program for commercial

clothes washers , i n its prior analysis 23 DOE estimated the percentage of market shares attributable to the existing ENERGY STAR program for residential clothes washers for 1996

– 2025. DOE assumed that commercial clothes washers would experience the same trend of annual increases in market p enetration beginning in 201 8 as the one presented by residential clothes

washers starting in 1997

. DOE added those percent increases to the market shares of commercial

clothes washers that met the target level in the RIA base case for each TSL , starting in 201 8

, to obtain the annual market shares of units meeting the efficiency level set for each TSL in the voluntary energy efficiency targets policy case. Table 17.3.7 summarizes DOE’s assumptions for commercial clothes washers regarding the market penetrati on of units in 2018 that, given voluntary energy efficiency targets, would meet the target efficiency level set for each TSL

.

Table 17.3.7 Market Penetrations in 2018 Attributable to V oluntary Energy E fficiency

T argets for Commercial Clothes Washers

Trial Standard Le vels

Front

- Loading 1 2 3 4

Base

- Case Market Share 31.0% 69.0% 69.0% 0.0% Policy Case Market Share 31.5% 69.5% 69.5% 0.5%

Increased Market Share 0.5% 0.5% 0.5% 0.5%
Top

- Loading 1 2 3 4

Base

- Case Market Share 26.7% 26.7% 11.3% 11.3% Policy Case Market Share 27.2% 27.2% 11.8% 11.8%

Increased Market Share 0.5% 0.5% 0.5% 0.5%

DOE estimated that the program developed in support of the voluntary energy efficiency

targets policy would increase market shares of ef ficient units

. Appendix 17

- A shows the ann ual market share increases due to this policy used as inputs to the NIA

- RIA model. Section 17.4

presents the resulting market penetratio n trends for the policy case of voluntary energy efficiency

targets for commercial clothes washers.

17

- 1517.3.6 EARLY REPLACEMENT

The non

- regulatory policy of early replacement refers to a program to replace commercial

clothes washers before the end of their useful lives. The purpose of such a policy is to replace old, inefficient units by units at the ta rget efficiency level defined for each TSL. DOE examin ed

several resources to inform its analysis of th e policy. One report detailed the Connecticut Appliance Retirement Program (ARP) , which was conducted in 2004. 24 Another was a 2006 study of the potentials for electric energy efficiency performed for the State of Vermont. 25

The Connecticut ARP was conducted from June through December 2004 by Nexus Market Research, Inc., and RLW Analytics, Inc., for Northeast Utilities

– Connecticut Light and Power and the United Illuminating Company’s Stat e programs. The purpose of the ARP was to help Connecticut utility customers overcome barriers to recycling room air conditioners (RACs), secondary refrigerators and freezers. The program picked up used appliances at customers’

homes or at turn

- in events, paid participants to retire their units, and educated customers about the costs of operating older appliances. In addition, the program provided consumers with financial incentives to replace inefficient RACs with ENERGY STAR

- qualified units.

Nexus/RLW use d program data and surveys to estimate the number of RACs retired by ARP participants, the percentage of retired units that were replaced with an ENERGY STAR model, and the number of RACs replaced by non

- participants during the program. According to the

Ne xus/RLW analysis, about 7 percent of all RACs retired during the program were retired through the ARP, and 63 percent of those were replaced with ENERGY STAR models. Thus the program resulted directly in about 4 percent of total eligible RAC consumers deci ding on early replacement of inefficient units.

In 2006, GDS Associates, Inc. conducted a study for the State of Vermont of the potential for reducing electricity use and peak demand through energy efficiency and fuel conversion measures. The study too k an aggressive, multi

- program approach, one aspect of which was early replacement of appliances. GDS considered that under the program residential appliances, including RACs, would be replaced during 4 years (2006

– 2009). GDS estimated achievable market pe netrations assuming that consumers would receive a financial incentive equal to 50 percent of the incremental cost of each program measure. GDS assumed a maximum 80

- percent penetration. For early replacement of RACs, GDS estimated a maximum achievable part icipation of 5 percent of eligible single

- family or multi

- family homes in the year before the program began (2005).

DOE also reviewed a study it had conducted in the 1990s, under EPACT 1992, which analyzed the effectiveness of Federal programs that suppo rt ed early retirement of appliances

. 26

Th e study identified policy options for early replacement that included a direct national program ;

17

- 16replacement of Federally

- owned appliances ; and promotion through product manufacturers, consumer incentives, incentives to utilities, and building regu lations. j

Historically, DOE modeled th e effects of the early replacement policy by increasing by 4 percent per year the retirement rate of units that were in the stock in the first year of the analysis peri od

. DOE chose the 4

- percent rate from the Connec ticut study because it was based on actual program experience. DOE assumed that the early replacement program would continue until it had facilitated the replacement of all eligible units in the stock in the year the program began

. S hipments of new units i n the year the program began and beyond would not be affected by th e

program , but would remain at base

- case efficiency levels. After the stock of inefficient units was completely replaced, the policy would produce no additional impacts.

F or the commercia l clothes washer RIA analysis, DOE analyzed a program designed to target installed units with efficiency levels lower than the target level set for each TSL and designed to encourag e their early replacement with equipment that perform s at that target level

. Rather than relying on a fixed rate of increase in retirement s , DOE based its estimates on t he economic feasibility of early replacement s. The feasibility depends on the energy efficiency of the unit being replaced, the purchase cost of a new efficient unit , and the energy cost savings. DOE performed the feasibility analysis of early replacements for front

- loading and top

- loading

commercial clothes washers.

DOE started the analysis estimating the stock of existing commercial clothes washers by vintage for each equipment class . To estimate th e stock by vintage DOE relied on the historical shipments from 198 9 to 201 1 used in the NIA (Chapter 10) . DOE then used the same lifetime distributions used in the NIA (Chapter 10) to estimate the existing stock by v intage for each of

the 30 year s that comprise the analysis period. As a second step to this analysis , DOE estimate d historical market efficiency distributions. DOE applied the historical market efficiency distributions to the stock by vintage that DOE proj ected. I t then estimated : (a) the market average unit lifetime energy cost of a commercial clothes washer from the existing stock, and (b) the unit lifetime energy cost of a replacement, efficient commercial clothes washer . DOE calculated both

values as th e discounted sum of the corresponding stream of energy costs, taking into account the age of the unit in the replacement year and a stream of annual probabilities that the unit will be in operation until the expected end of its life. The difference between the two lifetime energy costs DOE calculated reflects the estimated energy cost savings that a consumer is likely to enjoy from the early replacement of units in the existing stock by more efficient units . DOE calculated these savings for each of the 30 y ear s in the analysis period for commercial clothes washers from all vintages in the existing stock.

j The analysis concluded that, although cost

- effective opportunities for early replacement exist, a widespread Federal

program was not justified economically. Because early retirement means that a unit may be replaced by an appliance less efficient than the eventual replacement would have been, energy savings would be less than anticipated. Early replace ment programs also could increase long

- term sales volatility by encouraging a temporary increase in production, followed by a lull in demand. Early replacement could be economical in localities subject to high energy costs or environmental constraints; whe n replacement appliances are much more efficient than existing stock; or when a major technology breakthrough has occurred, creating the need for a ready market.

17

- 17Savings from early replacements, however, come at a cost. Anticipating the time of replacement of a unit in stock either (a) prevents consumers from reali zing financial benefits from investing the money they would pay for the new, replacement unit; or (b) imposes financial

costs to those consumers who do not hold funds

– and therefore would have to loan

– to purchase the new, replacement unit . In either cas e, there is an economic value in delaying the time of replacement. DOE calculated such value for commercial clothes washers from all vintages in the existing stock and for each year in the 30

- year analysis period.

While the energy cost savings are an inc entive for early replacing a unit in the existing stock ( benefits from replacing ), the financial costs underlying the early replacement ( benefits from waiting ) may hinder the initiative. DOE considered the net

- result from the benefits from replacing and th e benefits from waiting as an indicator of the decision that consumers would make towards anticipating the replacement of a front

- loading and a top

- loading commercial clothes washer in the existing stock by a more efficient unit rated at the target efficie ncy level set
for each TSL. The comparison of the two benefits shows that the early replacement of a unit

would be cost

- effective for f r ont

- loading units after their seventh year of operation, and for all

top

- loading u nit s

.

Based on the stock by vintage t hat DOE projected over the 30

- year analysis period, and

the net

- results from the benefits from replacing and the benefits from waiting for each vintage, DOE projected the number of potential front

- loading and top

- loading commercial clothes

washers in the e xisting stock to be (early) replaced in each of the 30 years of the analysis period. For this projection DOE further considered that, because of lack of information on the benefits from early replacements or any transaction costs involved in the process of early replacing a unit in operation, not all consumers would be informed to or willing to decide for the replacement. To

estimate the share of the market that would take such initiative DOE used the customized penetration curves it developed to support th e analysis of the rebate policy case described above

. DOE assumed for this policy case the same monetary incentive and market barrier level it estimated for each equipment class when calculat ing the impacts from rebates . As for the

benefit

- cost ratio , DOE used the ratio of the benefits from replacing divided by the benefits from waiting

.

The units in the existing stock projected to be replaced early represent additional shipments of front

- loading and top

- loading units rated at the target efficient level o f each TSL. Figure 17.3.2 shows DOE’s projections of shipments of front

- loading and top

- loading commercial clothes washers in response to an early replacement policy target ing the efficiency

levels set for the selected TSL

. An early replacement policy would create a fairly immediate jump in shipments of equipment that meet the target efficiency level relative to the base case. High

- efficiency units would be brought quic kly into the stock, leading to an immediate gain in

the mark et share of efficient units compared to the base case. However, a s opposed to the policy

cases discussed previously, an early replacement policy results in market shares of efficient units returning to base

- case percentages as the eligible market is deplet ed. Additionally , because units

removed early from the stock would have been replaced later (at the ends of their use ful lives)

17

- 18without the program, the number of shipments in later years drops slightly below the base

- case shipments forecast.

Front

- Loadi ng Top

- Loading

Figure 17.3.2 Base Case and Policy Case Shipments Project ion s for the Early Replacement Policy Case (thousand units)

DOE used the additional shipments for early replacements to calculate the increase in market penetration of more efficient units t hat would result from an early replacement policy.

Table 17.3.8 summarizes DOE’s assumptions for commercial clothes washers regarding the market penetration of units in 2018 meeting the target efficiency level at each TSL given early replacement incentives.

Table 17.3.8 Market Penetrations in 2018 Attributable to Early Replacement s for

Commercial Clothes Washers

Trial Standard Levels

Front

- Loading 1 2 3 4

Base

- Case Market Share 31.0% 69.0% 69.0% 0.0% Policy Case Market Share 32.9% 75.5% 75.5% 2.8%

Increased Market Share 1.9% 6.5% 6.5% 2.8%
Top

- Loading 1 2 3 4

Base

- Case Market Share 26.7% 26.7% 11.3% 11.3% Policy Case Market Share 35.3% 35.3% 25.6% 25.6%

Increased Market Share 8.6% 8.6% 14.3% 14.3%

The resulting increased market s hares attributable to early replacements shown in Table

17.3.8 were used as inputs in the NIA

- RIA model. Appendix 17

- A shows the annual market share increases due to this policy. Section 17.4 presents the resulting market penetration trends and cumulative savings for the policy case of early replacement for commercial clothes washers

.

17

- 1917.3.7 BULK GOVERNMENT PURC HASES

B ulk government purchases can lead to Federal, State, and local governmen ts purchasing large quantities of equipment that meet the target efficiency level . Combining the market

demands of mu ltiple public sectors also can provide a market signal to manufacturers and vendors that some of their largest customers seek equipment tha t meet an efficiency target at favorable p rices. Such a program also can induce “market pull,” whereby manufacturers and vendors would achieve economies of scale for high efficiency equipment

.

Most of the previous bulk government purchase (procurement) initiatives at the Federal, State, and municipal levels have not tracked data on numbers of purchases or degree of compliance with procurement specifications. In many cases, procurement programs are decentralized, being part of larger State or regional in itiatives. DOE based its assumptions regarding the effects of this policy on studies the Federal Energy Management Program (FEMP) performed regarding the savings potential of its procurement specifications for appliances and other equipment. FEMP, however, does not track purchasing data, because of the complex range of purchasing systems, large number of vendors, and so on . States, counties, and municipalities have demonstrated increasing interest and activity in “green purchasing." Although many of the

pro grams target office equipment, the growing infrastructure for developing and applying efficient purchasing specifications indicates that bulk government purchase programs are

feasible

. 27

, 28

DOE assumed that government agencies would administer bulk purchasing programs for

commercial clothes washers

. At the federal level, this type of program could modify the current FEMP procurem ent guidelines for commercial clothes washers, which refer to the ENERGY STAR requirements for commercial clothes washers 29

. DOE reviewed its own previous research on the potential for market transformation through bulk government purchases. Its major study analyzed several scenarios based on the assumption that 20 percent of Federal equipment purchas es in 2000 already incorporated energy efficiency require ments based on FEMP guidelines. One scenario in the DOE report showed energy efficient purchasing ram ping up during 10 years from 20 percent to 80 percent of all Federal purchases. 30 Bas ed on this study,

DOE estimated that a bulk government purchas e program instituted within a 10

- year period

would result in at least 80 percent of government

- purchased commercial clothes washers meeting target efficiency levels.

To e stimate the impact of a bulk government purchase program for this equipment , DOE first determined the shipments of commercial clothes washers that go to government

- owned housing. A report published by Lawrence Berkeley National Laboratory (LBNL) 31 in 20 12

estimated that around 3000

- 3200 commercial clothes washers were purchased annually for Federal buildings from 2005 through 2011

. T his correspond s to an average of 1.9% of all shipments of commercial clothes washers in the same period. DOE applied the estimated 1.9% of shipments to the forecast of market penetratio n of more efficient front

- loading and top

- loa ding

commercial clothes washers

. DOE estimated that , for each TSL, starting in 201 8 each year of a bulk government purchase policy would result in an increasing percent of shipments of

17

- 20government

- purchased units beyond the base case that would meet target efficiency level s . DOE estimated that within 10 years (by the end of 202 7 ), bulk government purchasing programs

would result in 80 percent of the front

- loading and top

- loading commercial clothes washers

market f or government

- owned commercial buildings meeting the target level of each TSL . DOE modeled the bulk government purchase program assuming that the market share for each equipment class achieved in 202 7 would be maintaine d throughout the rest of the analysis period.

Table 17.3.9 summarizes DOE’s assumptions for commercial clothes washers regarding

the market penetration of units in 2018 meeting , for each TSL, the target efficiency level given bulk government purchasing.

Table 17.3.9 Market Penetrations in 2018 Attributable to Bulk Government Purchasing o f

Commercial Clothes Washers

Trial Standard Levels

Front

- Loading 1 2 3 4

Base

- Case Market Share 31.0% 69.0% 69.0% 0.0% Policy Case Market Share 31.7% 70.3% 70.3% 0.1%

Increased Market Sh are 0.7% 1.3% 1.3% 0.1%
Top

- Loading 1 2 3 4

Base

- Case Market Share 26.7% 26.7% 11.3% 11.3% Policy Case Market Share 27.3% 27.3% 11.6% 11.6%

Increased Market Share 0.6% 0.6% 0.3% 0.3%

The resulting increased market shares attributable to bulk governm ent purchasing shown

in Table 17.3.9 were used as inputs in the NIA

- RIA model. Appendix 17

- A shows the annual market share increases due to this policy that DOE used as inputs to the NIA

- RIA model. Section

17.4 below presents t he resulting market penetration trends for the policy case of bulk government purchase of commercial clothes washers.

17.4 IMPACT S OF NON

- REGULATORY ALTERNATI VE S

Figure 17.4.1 and Figure 17.4.2 show the effects of each non

- regulatory policy on market penetration for front

- loading and top

- loading commercial clothes washers

. R elative to the base case, the policy cas es increase the market sha res that meet the target level. Re call the selected standards (not shown in the figures) would result in a 100

- percent market penetration of equipment that meet s the target efficiency level.

17

- 21Figure 17.4.1 Market Penetration of Front

- Loading Commercial Clothes Washers Meeting the Target Level in Poli cy Cases (TSL 2)

Figure 17.4.2 Market Penetration of Top

- Loading Commercial Clothes

Washers Meeting the Target Level in Policy Cases (TSL 2) 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%201820192020202120222023202420252026202720282029203020312032203320342035203620372038203920402041204220432044204520462047 Consumer Rebate

Consumer Tax Credit

Manufacturer Tax Credit

Voluntary Efficiency Targets

Govt Bulk Purchasing

Early Replacement

Base Case

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%201820192020202120222023202420252026202720282029203020312032203320342035203620372038203920402041204220432044204520462047 Consumer Rebate

Consumer Tax Credit

Manufacturer Tax Credit

Voluntary Efficiency Targets

Govt Bulk Purchasing

Early Replacement

Base Case

17

- 22Table 17.4.1 and Table 17.4.2 sho w the national energy savings and net present value for

six non

- regulatory policies analyzed in detail for commercial clothes washers . T he target level for each policy equals the efficiency level s proposed for standard s in TSL 2

. The case in which no regul atory action is taken with regard to commercial clothes washers constitute s the base case (or "No New Regulatory Action" scenario ) , in which NES and NPV are zero by definition. For comparison, the tables include the impacts of the proposed standard s. Energ y savings are given in quadrillion British thermal units (quads). The NPV s shown in Table 17.4.1 and Table 17.4.2

are based on two discount rates, 7 percent and 3 percent

.

The policies with the h ighest projected cumulative energy savings for front

- loading

commercial clothes washers are voluntary energy efficiency targets and early replacements , and for top

- loading are consumer rebates and voluntary energy efficiency targets

. Early replacements

res ult in negative NES for top

- loading commercial clothes washers, and negative NPV for both equipment classes and at both discount rates. The increase in shipments induced by the policy outweighs the benefits from the higher energy efficiency achieved in the policy case

, in relation

to the base case

, during the first years of the analysis period. For the remaining years, the decrease in shipments potentia t e s the benefits from the higher energy efficiency. However, the positive effects from the last years of t he analysis period do not counterbalance the negative effects observed over the initial years. In the case of top

- loading commercial clothes washers th is

is further amplified by the shipments increase in the policy case

, driven by substitution, which

event ually result s in negative NES

. NPV would become zero for front

- loading commercial

clothes washers at 2 .8 percent discount rate, and for top

- loading at 0.7 percent discount rate.

Table 17.4.1 Impacts of Non

- Regulatory Alternatives for Front

- Load i ng Commercial

Clothes W ashers (TSL 2 )

Policy Alternative Primary Energy Savings *

quads Net Present Value*

b illion 2013$

7% Discount

Rate 3% Discount

Rate

Consumer Rebates 0.0002 (2.8%) ** 0.001 0.003

Consumer Tax Credits 0.0007 (10.0%) 0.006 0.011

Manufacturer Tax Credits 0.0003 (5.0%) 0.003 0.006
Voluntary Energy Efficiency Targets 0.0047 (70.0%) 0.026 0.067
Early Replacement 0.0044 (65.2%)

- 0.089

- 0.009

Bulk Government Purchases 0.0003 (4.7%) 0.002 0.005
Proposed Standards 0.0067 (100.0%) 0.042 0.099

* For products s hipped 20 18

– 20 47

**The percentages show how the primary energy savings from each alternative policy compare to the savings from the proposed standards (represented in the table as 100%).

17

- 23Table 17.4.2 Impacts of Non

- Regulatory Alternatives for Top

- Load i ng Commercia l

Clothes Washers (TSL 2 )
Policy Alternative Primary Energy Savings *

quads Net Present Value*

b illion 2013$

7% Discount

Rate 3% Discount

Rate

Consumer Rebates 0.023 (37.6%) 0.076 0.163

Consumer Tax Credits 0.006 (10.1%) 0.027 0.049

Manufacturer Tax C redits 0.003 (5.0%) 0.013 0.024
Voluntary Energy Efficiency Targets 0.020 (32.4%) 0.055 0.133
Early Replacement

- 0.007 (

- 12.4%)

- 0.382

- 0.349

Bulk Government Purchases 0.001 (2.1%) 0.004 0.009
Proposed Standards 0.060 (100.0%) 0.201 0.433

* For produc ts shipped 20 18

– 20 47

**The percentages show how the primary energy savings from each alternative policy compare to the savings from the proposed standards (represented in the table as 100%).

17

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